Presentation	2009/12/14 Voice Activity Detection Using Dirichlet Prior Masakiyo Fujimoto, Shinji Watanabe, Tomohiro Nakatani,
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Abstract(in English)	This paper introduce the Dirichlet prior into a statistical model-based voice activity detection(VAD), and shows its advantage. The statistical model-based VAD identify speech/non-speech period based on environmental adapted speech and non-speech GMMs which are constructed by the Switching Kalman filter. The constructed GMMs include important and unimportant Gaussian distributions. Thus, the performance of VAD can be improved by reducing unimportant Gaussian distribution. Here, prior probabilities of each remaining distribution may drastically change, because the distribution shape after the Gaussian reduction is much different from original GMM. Thus, we propose an optimization method of prior probabilities by using the Dirichlet prior.
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Committee	NLC
Conference Date	2009/12/14(1days)
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Registration To	Natural Language Understanding and Models of Communication (NLC)
Language	JPN
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Title (in English)	Voice Activity Detection Using Dirichlet Prior
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1st Author's Name	Masakiyo Fujimoto
1st Author's Affiliation	NTT Communication Science Laboratories, NTT Corporation()
2nd Author's Name	Shinji Watanabe
2nd Author's Affiliation	NTT Communication Science Laboratories, NTT Corporation
3rd Author's Name	Tomohiro Nakatani
3rd Author's Affiliation	NTT Communication Science Laboratories, NTT Corporation
Date	2009/12/14
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Volume (vol)	vol.109
Number (no)	355
Page	pp.pp.-
#Pages	6
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